Systems and methods for providing hi-fidelity contextual search results

ABSTRACT

Systems and methods for providing hi-fidelity contextual search results are described. In one described embodiment, a method for providing hi-fidelity contextual search results includes receiving a query comprising a search term, determining a location on a page that is responsive to the query, wherein the page has a native appearance, determining a contextual area associated with the location on the page, and causing the contextual area to be output in a hi-fidelity result set, wherein the contextual area has an appearance the same as the native appearance of the page.

FIELD OF THE DISCLOSURE

Embodiments of the disclosure relate generally to indexing and retrievalof pages. More particularly, embodiments of the disclosure relate tosystems and methods for providing hi-fidelity contextual search results.

BACKGROUND

Computer software applications for searching and providing results ofsearches are known in the art. Such applications include Internet searchengines, such as those provided by Google® and Yahoo®; documentmanagement systems, such as ones provided by Interwoven®; andInternet-based document management systems, such as the Share serviceprovided by Adobe®.

Conventional software applications for searching document collectionsand providing results of the searches are typically text-based. Forexample, some conventional applications allow a user to input searchcriteria using an input device, such as a keyboard, and return thesearch results as a web page containing text. Some such applicationsreturn contextual search results. For instance, conventionalapplications may return a set of search results with the search termshighlighted or otherwise emphasized within the search result list.

In order to respond to search requests, conventional search applicationsrely on some form of indexing. For example, conventional systems mayemploy a software application known as a “crawler” to traverse a set ofweb pages and other content. This content may be local or may bedistributed across a network, such as the Internet. Once the crawler hastraversed the content, it stores information about the content,including its location, in an index.

SUMMARY

Embodiments of the disclosure provide systems and methods for providinghi-fidelity contextual search results. One embodiment is a methodcomprising receiving a query comprising a search term; determining alocation on a page that is responsive to the query, wherein the page hasa native appearance; determining a contextual area associated with thelocation on the page; and causing the contextual area to be output in ahi-fidelity result set, wherein the contextual area has an appearancethe same as the native appearance of the page.

Another embodiment is a method comprising receiving a page having anative appearance; identifying a keyword on the page; generating anindex entry having the keyword, a page identifier, and a coordinatelocation associated with the keyword on the page; storing the indexentry; and rendering an image of at least the coordinate location on thepage associated with the keyword, wherein the image has the nativeappearance of the page. In other embodiments, a computer-readable medium(such as, for example, random access memory or a computer disk)comprises code for carrying out these methods.

These embodiments are mentioned not to limit or define the disclosure,but to provide examples to aid understanding thereof. Embodiments arediscussed in the Detailed Description, and further description isprovided there. Advantages offered by the various embodiments may befurther understood by examining this specification.

BRIEF DESCRIPTION OF THE DRAWINGS

These and other features, aspects, and advantages of the presentdisclosure are better understood when the following Detailed Descriptionis read with reference to the accompanying drawings, wherein:

FIG. 1 is a diagram illustrating an exemplary environment forimplementation of one embodiment;

FIG. 2 is a flowchart illustrating the provision of search results inone embodiment;

FIG. 3 is a flowchart illustrating an indexing function in oneembodiment;

FIG. 4 is a depiction of an image of a page created according to oneembodiment;

FIGS. 5 a and 5 b are flowcharts illustrating a rendering function inone embodiment; and

FIG. 6 is a depiction of a hi-fidelity result set provided by oneembodiment.

DETAILED DESCRIPTION

Embodiments of the disclosure provide systems and methods for providinghi-fidelity contextual search results.

Illustrative Embodiment of Hi-Fidelity Contextual Search

In one illustrative embodiment, a server dispatches a crawler togenerate an index of documents. As the documents are indexed, the serverdetermines the file type of the document (e.g., Microsoft Word® formator Adobe Portable Document Format® (“PDF®”)). In one embodiment, if theserver determines that the file type is not PDF®, then the serverconverts the document to PDF® format.

As part of indexing the document, the server determines the location onthe document of one or more words. The server stores this location alongwith various other information about the document, including, forexample, a unique identifier for the document and the word itself. Theserver may also store structural information regarding each document,including, for example, the layout of the document (e.g., a web pagethat includes columns).

In one illustrative embodiment, the server also performs a renderingprocess. During the rendering process, the server creates an image fromeach page in a document. Many document formats, such as PDF® andMicrosoft Word®, divide documents into pages. However, the term “page,”as used herein, should not be so limited. For example, an entiredocument of any format, or any portion thereof, can comprise a “page.”Further, a portion of a page can comprise a “page.” Therefore, the term“page” should not be construed as limiting the scope of the disclosure.The locations stored by the indexing process described above correspondto locations on the document of each word in the each image that iscreated during the rendering process.

Once the documents have been indexed and rendered, a user can search forkeywords that may appear in the documents. In one illustrativeembodiment, the user enters a query and submits it to a server. Inresponse to receiving the query, the server searches the index forkeyword(s) that satisfy the query and identifies one or more pageswithin the documents that contain content that is responsive to thequery. The server next identifies the location on the page image thatcontains the responsive content, i.e., the keyword that is relevant tothe user's query. The server also identifies a contextual area aroundthe location based on properties of the page. The server then extractsthe contextual area and renders it as an image. The server next providesthe image to the user as part of a hi-fidelity result set. The image ofthe contextual area provided as part of the hi-fidelity result set hasthe same native appearance as the page. The native appearance mayinclude the same formatting, fonts, and/or graphics present on the page.

This illustrative example is given to introduce the reader to thegeneral subject matter discussed herein. The disclosure is not limitedto this example. The following sections describe various additionalembodiments and examples of methods and systems for providinghi-fidelity contextual search results.

Illustrative Environments for Providing Hi-Fidelity Search Results

Referring now to the drawings, in which like numerals indicate likeelements throughout the several figures, FIG. 1 is a diagramillustrating an exemplary environment for implementation of oneembodiment. Other embodiments may be utilized. The embodiment shown inFIG. 1 includes a server 100 that comprises a processor 110 and a memory120. In the memory 120 are stored applications, including a web crawler130, an indexing application 140, and a search engine 150. Suchapplications may be resident in any suitable computer-readable mediumand executable on any suitable processor. Such processors may comprise,for example, a microprocessor, an ASIC, a state machine, or otherprocessor, and can be any of a number of computer processors, such asprocessors from Intel Corporation, Advanced Micro Devices Incorporated,and Motorola Corporation. The computer-readable media storesinstructions that, when executed by the processor, cause the processorto perform the steps described herein.

Embodiments of computer-readable media comprise, but are not limited to,an electronic, optical, magnetic, or other storage device, transmissiondevice, or other device that comprises some type of storage and that iscapable of providing a processor with computer-readable instructions.Other examples of suitable media comprise, but are not limited to, afloppy disk, CD-ROM, DVD, magnetic disk, memory chip, ROM, RAM, PROM,EPROM, EEPROM, an ASIC, a configured processor, all optical media, allmagnetic tape or other magnetic media, or any other medium from which acomputer processor can read instructions. Also, various other forms ofcomputer-readable media may be embedded in devices that may transmit orcarry instructions to a computer, including a router, private or publicnetwork, or other transmission device or channel, both wired andwireless. The instructions may comprise code from any suitablecomputer-programming language, including, for example, C, C++, C#,Visual Basic, Java, Python, Perl, and JavaScript.

In a further embodiment, each of the processes performed by the server100 is performed on a separate server, to with: there are separateindexing, rendering, and display servers. In still further embodiments,multiple servers are used to perform various tasks, including, forexample indexing, rendering, and search engine. In such embodiments,techniques such as clustering or high availability clustering may beused. Benefits to architectures such as these include redundancy andperformance, among others.

In the embodiment shown, the server 100 is in communication with a datastore 200, which includes an image database 210, an index database 220,and a structural database 230. In an alternative embodiment, a singledatabase contains image, structural, and index data. In furtherembodiments, structural data, and/or index data, and/or images arestored across multiple databases.

The server 100 is also in communication with other external servers 300via a network 400. Further, the server 100 is in communication with auser's computer 500 via a network 600. The networks 400, 600 may be anyof a number of public or private networks, including, for example, theInternet, a local area network (“LAN”), or a wide area network (“WAN”).In one embodiment, a single computer that is not connected to a networkis searched in order to index documents on that computer. Such anembodiment may be utilized as a search appliance. The documents locatedon the external servers 300 may be in a variety of formats, including,for example, Hypertext Markup Language (“HTML”), XML, PDF®, MicrosoftWord® Document format, plain text, and rich text.

One embodiment includes a crawler 130. The crawler 130 methodically andautomatically traverses computers 300 in communication with a network400 searching for documents. When the crawler 130 finds a document, itdownloads the document. In some embodiments, the crawler 130 traversescomputers 300 on a network 400 searching for documents based on formatof the document. For instance, the crawler 130 may search for alldocuments of a format in an inclusion list or for documents that are ofany format except those named in an exclusion list. In one embodiment,when the crawler 130 finds a document, it downloads the document andstores it on the server 100. In another embodiment, the crawler 130temporarily saves the document into an image database 210.

In the embodiment shown in FIG. 1, once the crawler 130 has located oneor more documents, an indexing application 140 processes the documents.In one such embodiment, the indexing application 140 processes onedocument at a time. First, the indexing application 140 converts adocument to PDF® format, and then renders each page of the document intoan image. The indexing application 140 next processes each individualword in the document. For each word, the indexing application 140identifies and stores in an index database 220 a set of coordinatesdefining a rectangle surrounding the word. In on one such embodiment,the indexing application 140 also identifies and stores structuralinformation about the page in a structural database 230. The indexingapplication 140 may skip words included in an exclusion list, such asstop words. In a further embodiment, the indexing application 140processes words included in an inclusion list.

In one embodiment, a search engine 150 retrieves and provides a resultlist in response to a query. The search engine 150 searches the indexdatabase 220 to determine whether it includes information regardingpages that are responsive to the query. Next, if the search of the indexdatabase 220 returned one or more pages that are responsive to thequery, the search engine 150 retrieves one or more images from the imagedatabase 210. Further, the search engine 150 uses information retrievedfrom the structural database 230 to determine how to provide the searchresults. The search engine 150 renders an image comprising the searchresults as a part of a hi-fidelity result set. The hi-fidelity resultset comprises the image so that the portion of the results presented tothe user is in its native appearance—e.g., with the same formatting,fonts, graphics, and/or other distinguishing features present on theoriginal page. Thus, it is a hi-fidelity search result. In oneembodiment, the hi-fidelity result set includes contextual data. Forinstance, in one such embodiment, the search terms that the userprovided are highlighted in the hi-fidelity result set.

In the embodiment shown in FIG. 1, the search engine 150 transmits thehi-fidelity result set over the network 600 to the user's computer 500.As with the network 400, the network 600 can include a local areanetwork (“LAN”), a wide area network (“WAN”), or the Internet, amongothers. In one embodiment, the hi-fidelity result set is displayed on aweb browser executed on the user's computer 500. The computer 500 canbe, for example, a personal computer (“PC”), UNIX or Linux workstation,thin, thick, or smart client, or other device capable of receiving ahi-fidelity result set. The web browser is a software program such asMicrosoft Internet Explorer® or Mozilla Firefox®.

In the embodiment shown in FIG. 1, the server 100 is in communicationwith a data store 200 comprising a plurality of databases 210, 220, and230. The Data Store 200 resides on a computer-readable medium, such asdescribed above. In one embodiment, the Data Store 200 further comprisesa database management system. The database management system performstasks, such as controlling the organization, storage, management, andretrieval of data in the databases. Examples of database managementsystems include Oracle Database® offered by Oracle Corporation, DB2®offered by International Business Machines Corporation, Microsoft SQLServer® offered by Microsoft Corporation, and Sybase Adaptive ServerEnterprise® offered by Sybase Incorporated. In a further embodiment, theimage database 210, index database 220, and structural database 230reside on separate data stores.

In the embodiment shown, the image database 210 includes one or moreimages. Each image represents one page of a document. The image database210 further stores information regarding each image, such as thedocument to which it belongs and which page in the document the imagerepresents. In one embodiment, the image database 210 also includes aunique page identifier for each image. The page identifier allows eachimage to be referenced and identified. In other embodiments, the imagedatabase 210 comprises images representing a portion of a page or morethan one page of a document. For example, the image database 210 maycomprise images representing an entire document or some portion thereof.

In the embodiment shown in FIG. 1, the index database 220 includes wordsthat are present on a page as well as location coordinates for one ormore rectangles, each of which defines an area of the page that includesthe particular word. The index database 220 further includes informationsufficient to identify the image that represents the page. For example,in one embodiment, the index database 220 includes a page identifier. Ina further embodiment, the index database 220 includes words present onmore than one page of a document. For example, the index database 220may include words present on an entire document or some portion thereof.

The embodiment shown in FIG. 1 also comprises a structural database 230.The structural database 230 stores information regarding the structureof a page. This structural information may include information aboutsuch page elements as columns, lists, and images. For instance, in onesuch embodiment, a document containing two-column print would beidentified as such in the structural database 230. In one embodiment,the structural database identifies the structural elements present on apage and provides coordinates of a rectangle or other polygon enclosingeach identified structural element. In a further embodiment, thestructural database 230 includes information about a subset of theelements present on a page. In some embodiments, a user can customizethe structural elements about which the structural database 230 storesinformation. In one embodiment, the structural database 230 includes apage identifier. In a further embodiment, the structural database 230includes structural elements present on more than one page of adocument. For example, the structural database 230 may includestructural elements present on an entire document or some portionthereof.

In further embodiments, a single database contains structural and indexdata. In still further embodiments, a single database containsstructural and index data, as well as images. In still furtherembodiments, structural data, and/or index data, and/or images arestored in multiple databases.

Illustrative Methods for Providing Hi-Fidelity Search Results

FIG. 2 is a flowchart illustrating the provision of search results inone embodiment. FIG. 2 is described herein in reference to theillustrative environment shown in FIG. 1. However, the process is notlimited to execution within that environment. In the embodiment shown, asearch engine 150 receives a query 1000 comprising a search term. Invarious embodiments, the search may originate from a web pagespecifically designed to provide search capability or may originate froman application that includes integrated search capability.

In response, the search engine 150 searches the index for the searchterm 1100. For instance, in one embodiment, the search engine 150generates a Structured Query Language (“SQL”) statement for use insearching the index database 220. The search engine 150 identifies apage (or pages) satisfy the search criteria.

After identifying a page satisfying the search criteria, the searchengine 150 determines what portion of a page contains content matchingthe criteria 1200. For instance, in one embodiment, the search engine150 determines the coordinates of a polygon, such as a rectangle, thatcontains one or more of the search terms.

The embodiment shown in FIG. 2, the search engine 150 next determinesthe coordinates of contextual data within the polygon 1300. Variousembodiments extract different amounts of contextual data. For example,one embodiment extracts one line of contextual data above the linecontaining the search term and one line of contextual below the linecontaining the search term. One embodiment uses structural informationabout the page to determine which contextual data and what amount ofthat contextual data to extract. Another embodiment allows a user oradministrator to specify the amount and/or type of contextual dataextracted when a search term is located.

Some embodiments include computer program code to process “edgecases”—i.e., situations that arise when a search term occurs near theedge of a page or page component, e.g., a word appearing at the bottomor top of a page. For instance, in one embodiment, if the search term isfound at the beginning of a document, the contextual data includes twolines after the line containing the search term. In contrast, if thesearch term is found at the end of a document, the contextual dataincludes two lines before the line containing the search term. If thesearch term is found on the last line of a page, but not the last lineof a document, the contextual data includes the first line on the nextpage. If the search term is found on the first line of a page, but notthe first line of a document, the contextual data includes the last lineon the previous page. If the search term is found in text that iswrapped around an image, the contextual data includes the image, or, ifit is a large image, the contextual data includes a portion of the imagethat is in line with the text. There are many other cases in whichstructural information may be utilized to determine which contextualdata to display. The preceding examples are by no means comprehensive;they are merely representative.

After determining the coordinates of the contextual data, the searchengine 150 extracts the portion of the page bounded by the coordinatesdetermined in step 1200 and saves the extracted portion as an image.Next, the search engine 150 highlights the search term and renders animage of the contextual data 1400. These images can be of JPEG, GIF,bitmap, or any other image format.

Once the image has been rendered, the search engine 150 generates ahi-fidelity result set that includes the rendered image 1500. In someembodiments, the steps 1100-1500 are repeated for multiple documents orportions of documents that satisfy the search criteria. Once the searchengine 150 has completed generating the search results, or a page of thesearch results, the search engine 150 transmits those results to theuser's computer 500 from which the search query originated.

Illustrative Methods for Providing an Indexing Function

FIG. 3 is a flowchart illustrating an indexing function in oneembodiment. In the embodiment shown, the indexing application 140receives a document 2000, which can be of any format. Examples offormats that the embodiment can receive include Microsoft Word®document, HTML, PDF, rich text, plain text, XML, and many others thatare known in the art.

In the embodiment shown, the indexing application 140 next determineswhether the format of the document is PDF® 2100. For instance, theindexing application may evaluate the file extension or examine thecontents of the file.

If the format is not PDF®, the indexing application 140 converts thedocument to PDF® 2110. For instance, the indexing application 140 mayutilize a converter or distiller to perform the conversion. In theembodiment shown, the remaining steps of the process are performed onthe original (native format) or the resulting PDF® document. In otherembodiments, the native format is used in the subsequent steps of theprocess.

In the embodiment shown in FIG. 3, the indexing application 140 nextdetermines whether the PDF® document includes multiple pages 2200. Ifthe PDF® document includes multiple pages, then the indexing application140 separates the document into individual pages 2210. In otherembodiments, the document may be stored as a single multi-page documentrather than being separated.

Once a page has been analyzed, the indexing application 140 determineswhether any additional pages remain to be processed 2300. If no morepages are left to be processed, processing is terminated 2310.

However, in the embodiment shown, if a page remains to be processed2300, the indexing application 140 selects the next page to be processed2350. The processing of pages continues until all of the pages of thedocument have been processed.

The indexing application 140 may process the documents in a variety ofways. For instance, in the embodiment shown in FIG. 3, the indexingapplication 140 reads each word in the page, and then performs thefollowing steps for each word. The indexing application 140 firstdetermines if any additional words remain on the page to be read 2400.If so, the indexing application 140 reads the next word 2500. Theindexing application then compares the word to an exclusion list 2600.For instance, in one embodiment, the exclusion list contains words suchas “a,” “an,” and “the,” that appear frequently on pages. These types ofwords are often referred to as “stop” words. Further embodiments includeexclusion lists containing words in languages other than English. Inanother embodiment, the indexing application 140 compares the word to aninclusion list.

Some embodiments also store structural information about a page to aidin the hi-fidelity rendering of the page. For example, in the embodimentshown in FIG. 3, the indexing application 140 identifies and storesstructural information about the page 2410. Structural information mayinclude, for example, information about lists, columns, margins, images,and other structural information on the page. For instance, a documentmay include text in two columns. The text in one column may wrap aroundimages within the document. This information can be used to determinehow best to provide results from a specific portion of a page within thedocument. In a further embodiment, the information stored comprises thestructural element, a page identifier, and a location on the page.

In the embodiment shown in FIG. 3, an image of the page is rendered andstored 2420. The indexing application 140 may utilize known software torender the page into an image of one of many formats, such as GIF, JPEG,TIFF, bitmap, and others. In one embodiment, the image has the nativeappearance of the page. Each rendered page is stored in the imagedatabase 210.

In the embodiment shown in FIG. 3, if the indexing application 140determines that the word is not in the exclusion list and thus should beindexed, the indexing application 140 determines a coordinate locationassociated with the word on the page. The coordinate location comprisesthe coordinates of a rectangle enclosing the word on the page 2700. Inexisting applications, an indexing process may determine the location ofa word by means of an offset, which represents the number of charactersbefore the first letter of the word. But, such applications haveshortcomings; for example, they fail to account for the structure,format, and fonts of the page. In order to overcome these shortcomings,in some embodiments the indexing application 140 determines thecoordinate location of the word based on its actual position on thepage, not merely in relation to other words. More specifically, someembodiments determine the coordinates of a rectangle enclosing the word.To determine the rectangle coordinates, the indexing application 140locates the word on the page.

In one such embodiment, the indexing application 140 determines thecoordinates of the top-left corner of the word and the coordinates ofthe bottom-right corner of the word. Thus, the points represented by thetop-left and bottom-right corners of the word also constitute thetop-left and bottom-right corners of the rectangle enclosing the word.In a further embodiment, the coordinates of the top-left andbottom-right corners of the rectangle are offset from the respectivecorners of the word. For example, the top-left corner of the rectanglemay be one or more pixels above and one or more pixels to the left ofthe top-left corner of the word. Similarly, the bottom-right corner ofthe rectangle may be one or more pixels below and one or more pixels tothe right of the bottom-right corner of the word. One benefit of such anembodiment is that it takes into account the fact that letters are notof a uniform height.

In another embodiment, the indexing application 140 determines thecoordinates of the top-left corner of the word and the length and heightof the word. As discussed above, the coordinates of the top-left cornerof the word are used to determine the coordinates of the top-left cornerof a rectangle enclosing the word. Next, the indexing application 140determines the location and dimensions of the rectangle based on thelength and height of the word. In one embodiment, the length and heightof the sides of the rectangle are based on an offset from the length andheight of the word. One benefit of such an embodiment is that it takesinto account the fact that letters are not of a uniform height.

The location and dimensions of the rectangles described above can berepresented by various means. In one embodiment, the indexingapplication 140 uses pixels to identify the coordinates. Furtherembodiments use units of measurement, such as millimeters and inches toidentify coordinates, length, and height. For example, one embodimentidentifies the top-left coordinate of a word based on its distance fromthe top-left corner of the page on which the word is found. Furtherembodiments use pixels to represent coordinates and units ofmeasurement, such as millimeters and inches, to represent length andheight.

In some cases, a word may not reside entirely on a single line. In suchan instance, one embodiment identifies the coordinates of a firstrectangle enclosing the first portion of the word and identifiescoordinates of a second rectangle enclosing the second portion of theword, using the techniques described above for each rectangle. If a wordis broken across more than two lines, one embodiment identifiescoordinates of rectangles enclosing each portion of the word, using theaforementioned techniques.

In one embodiment, after identifying coordinates of one or morerectangles enclosing a word, indexing application 140 stores the indexdata 2800. The index data may be stored in an index entry having a word,a page identifier, and a location on the page associated with the word.In one embodiment, the location on the page associated with the wordincludes the coordinates of one or more rectangles enclosing the word.In the embodiment shown, after storing index data, the indexingapplication 140 determines whether any more words remain on the page tobe processed 2400, and repeats the above-described process for eachword.

FIG. 4 is a depiction of an image of a page created according to oneembodiment. In particular, FIG. 4 represents an image of a pageretrieved by the crawler 130 and rendered by the indexing application140. The word “bargaining” is enclosed with a rectangle 3000, thecoordinates of which were determined according to one embodiment, usingthe techniques described above.

Illustrative Methods for Providing a Rendering Function

FIGS. 5 a and 5 b are flowcharts illustrating the rendering function inone embodiment. More particularly, FIG. 5 a illustrates the rendering ofa page. In the embodiment shown, the indexing application 140 performsthis process for each page in a document. In one embodiment, theindexing application 140 receives a page 3000. This page may be in PDF®format. In further embodiments, the page is of various formats, such astext, Microsoft Word® format, HTML format, XML format, or any otherformat.

After receiving the page 4000, the indexing application 140 converts thepage to an image format 4100, such as JPEG format. In anotherembodiment, the page is converted to a TIFF format. Further embodimentsconvert the page to various other formats, including GIF, bitmap, or anyother image format. In one embodiment, the image has the nativeappearance of the page.

After converting the page to an image format, the indexing application140 stores the image 4200 onto a computer-readable medium. In oneembodiment, the image is stored on a disk drive. In further embodiments,the image is stored on tape, optical, or any other computer-readablemedium. It is beneficial to store the image with identifying informationin order to, for example, facilitate finding and retrieving the image.Thus, in one embodiment, the image is stored along with identifyinginformation. In one such embodiment, the identifying informationincludes a document identifier and a page identifier.

There are a number of benefits to be derived from rendering a pageaccording to embodiments. These benefits include increased speed andefficiency in finding and retrieving pages containing search terms. Inparticular, the ability to retrieve a single rendered page yields betterperformance than retrieving an image comprising an entire documentcontaining multiple pages.

In order to provide contextual search results, it is first necessary torender a portion of a page containing contextual data. FIG. 5 billustrates the rendering of a portion of a page or pages. Oneembodiment performs this process in order to provide contextual searchresults. First, the search engine 150 receives an image of a page andcoordinates 5000. In some cases, the contextual data spans more than oneimage. In such an instance, each image containing contextual data issent. Further, one set of coordinates is sent corresponding to eachimage that is sent. Thus, if two images are sent, then two sets ofcoordinates are sent (i.e., one set of coordinates for each image).

After receiving the page and coordinates, the search engine 150identifies the portion of the image corresponding to the area bound bythe coordinates. Next, the application extracts that portion of theimage 5100. In one embodiment, if more than one image is sent, then thesearch engine 150 extracts a portion of each image corresponding to eachset of coordinates.

After extracting the portion of the image corresponding to the areabound by the coordinates, it is beneficial to highlight the search term.In the embodiment depicted in FIG. 5 b, the search engine 150 highlightsthe search term 5200. In one embodiment, the highlighting step involvesapplying a color (e.g., yellow, pink) to the area bounded by therectangle enclosing the search term.

After highlighting the search term, it is beneficial to render thecontextual search results into an image. According to the embodimentdepicted in FIG. 5b, the search engine 150 converts the portion orportions of the image or images extracted in step 5100 and highlightedin step 5200 into an image 5300. In one embodiment this image is of aJPEG format. In further embodiments, the image is of TIFF, GIF, bitmap,or any other image format.

Finally, after rendering the contextual search results into an image theimage is provided as part of a hi-fidelity result set. In the embodimentdepicted in FIG. 5 b, the search engine 150 provides the image to a useras a hi-fidelity result set 4400.

FIG. 6 is a depiction of a hi-fidelity result set provided by oneembodiment. In FIG. 6, a user searched for the term “bargaining,” andthe search engine 150 in one embodiment provided a hi-fidelity resultset 6000 comprising contextual search results in response to the user'squery. In the embodiment depicted in FIG. 6, the search term ishighlighted 6100. It is apparent from FIG. 6 that the result set ishi-fidelity because it has the same native appearance as the originalimage of the document, depicted in FIG. 4.

Benefits of rendering a portion of a page include increased speed andefficiency in providing images containing contextual search results. Inparticular, the ability to provide a comparatively small imagecomprising the contextual search results yields better performance thanproviding an image representing an entire page.

Providing hi-fidelity contextual search results has numerous benefits,including showing search results to a user in context. A further benefitof embodiments of the disclosure is the ability to display the searchresults in hi-fidelity. Such embodiments display contextual searchresults with the same native appearance as in the original document. Forexample, in one embodiment, contextual search results appear in the samefont and with the same formatting as they appear in the original page. Abenefit of this feature is that when a user views multiple searchresults in hi-fidelity, he or she may recognize a particularly pertinentresult because of its font, formatting, or the like. This may beparticularly beneficial when a user is searching for a page that he orshe has viewed previously.

The foregoing description of the embodiments of the disclosure has beenpresented only for the purpose of illustration and description and isnot intended to be exhaustive or to limit the disclosure to the preciseforms described. Numerous modifications and adaptations are apparent tothose skilled in the art without departing from the scope of thedisclosure.

1. A computer-implemented method comprising: receiving a query comprising a search term; determining a location on a page that is responsive to the query, wherein the page has a native appearance; determining a contextual area associated with the location on the page, wherein determining a contextual area comprises: determining a plurality of lines of text associated with the location on the page, wherein determining a plurality of lines of text associated with the location on the page comprises: extracting a line of text above the line of text containing the search term; and extracting a line of text below the line of text containing the search term; identifying coordinates of a polygon including the lines of text; rendering the contextual area into an image; and in response to the query, and without requiring any additional user action, causing the image to be output in a hi-fidelity result set displaying one or more results of the query, wherein the image has an appearance the same as the native appearance of the page.
 2. The method of claim 1, wherein the appearance of the image comprises at least one of formatting, fonts, or graphics the same as or similar to at least one of formatting, fonts, or graphics of the native appearance of the page.
 3. The method of claim 1, wherein causing the image to be output in a hi-fidelity result set comprises: displaying the image.
 4. The method of claim 1, further comprising highlighting the search term in the contextual area.
 5. The method of claim 1, wherein the contextual area is pre-rendered.
 6. The method of claim 1, wherein the page comprises a format including at least one of: hypertext markup language, extensible markup language, portable document format, Microsoft Word Document format, plain text, or rich text.
 7. A non-transitory computer-readable medium embodying computer program code for displaying search results, the computer program code comprising computer executable instructions, comprising: receiving a query comprising a search term; determining a location on a page that is responsive to the query, wherein the page has a native appearance; determining a contextual area associated with the location on the page, wherein determining a contextual area comprises: determining a plurality of lines of text associated with the location on the page, wherein determining a plurality of lines of text associated with the location on the page comprises: extracting a line of text above the line of text containing the search term; and extracting a line of text below the line of text containing the search term; identifying coordinates of a polygon including the lines of text; rendering the contextual area into an image; and in response to the query, and without requiring any additional user action, causing the image to be output in a hi-fidelity result set displaying one or more results of the query, wherein the image has an appearance the same as the native appearance of the page.
 8. The computer-readable medium of claim 7, wherein the appearance of the image comprises at least one of formatting, fonts, or graphics the same as or similar to at least one of formatting, fonts, or graphics of the native appearance of the page.
 9. The computer-readable medium of claim 7, wherein causing the program code for causing the contextual area to be displayed in a hi-fidelity result set comprises: program code for selecting the contextual area; program code for rendering the contextual area into an image; and program code for displaying the image.
 10. The computer-readable medium of claim 7, further comprising program code for highlighting the search term in the contextual area.
 11. The computer-readable medium of claim 7, further comprising program code for pre-rendering the contextual area.
 12. The computer-readable medium of claim 7, wherein the page comprises a format including at least one of: hypertext markup language, extensible markup language, portable document format, Microsoft Word Document format, plain text, or rich text.
 13. The computer-readable medium of claim 7, wherein the program code for displaying search results comprises an Internet search engine.
 14. The computer-implemented method of claim 3, wherein the image comprises a format including at least one of: JPEG, GIF, or bitmap.
 15. The non-transitory computer-readable medium of claim 9, wherein the image comprises a format including at least one of: JPEG, GIF, or bitmap.
 16. The computer-implemented method of claim 1, wherein causing the contextual area to be output in a hi-fidelity result set comprises converting the contextual area from a first format to a second format.
 17. The non-transitory computer-readable medium of claim 7, wherein causing the contextual area to be output in a hi-fidelity result set comprises converting the contextual area from a first format to a second format.
 18. The computer-implemented method of claim 1, wherein causing the contextual area to be output as a hi-fidelity result set comprises causing the contextual area to be displayed simultaneously with a search result.
 19. The non-transitory computer-readable medium of claim 7, wherein causing the contextual area to be output as a hi-fidelity result set comprises causing the contextual area to be displayed simultaneously with a search result.
 20. The computer-implemented method of claim 1, wherein the result set comprises a plurality of results, each corresponding to a respective result of the query, and wherein the contextual area is displayed as a portion of one of the results of the query displayed in the result set.
 21. The non-transitory computer-readable medium of claim 7, wherein the result set comprises a plurality of results, each corresponding to a respective result of the query, and wherein the contextual area is displayed as a portion of one of the results of the query displayed in the result set.
 22. The computer-implemented method of claim 1, wherein the image having an appearance the same as the native appearance of the page comprises the image comprising text in a same font as text in the native appearance of the page.
 23. The computer-implemented method of claim 1, wherein the image having an appearance the same as the native appearance of the page comprises the image comprising a graphic that is the same as a graphic in the native appearance of the page.
 24. The computer-implemented method of claim 1, wherein determining a location on a page that is responsive to the query comprises determining coordinates of a polygon that contains the search term.
 25. The computer-implemented method of claim 1, wherein rendering the contextual area into an image comprises: extracting the portion of the image corresponding to the area bounded by the coordinates of the polygon; highlighting the search term in the contextual area; and converting the extracted portion of the image into an image format. 